import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]							)   ->    Optional[Any]:
		_lowerCamelCase				   =  [
		    'decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(UpperCamelCase			,       UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]							)   ->    int:
		_lowerCamelCase   ,							_lowerCamelCase				   =  emb.weight.shape
		_lowerCamelCase				   =  nn.Linear(UpperCamelCase			,       UpperCamelCase			,       bias=UpperCamelCase							)
		_lowerCamelCase				   =  emb.weight.data
		return lin_layer


def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    int:
		_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location='cpu'							)
		_lowerCamelCase				   =  Namespace(**checkpoint['cfg']['model']							)
		_lowerCamelCase				   =  checkpoint['model']
		remove_ignore_keys_(UpperCamelCase							)
		_lowerCamelCase				   =  state_dict['decoder.embed_tokens.weight'].shape[0]

		_lowerCamelCase				   =  {key.replace('decoder'			,       'model'							): val for key, val in state_dict.items()}

		_lowerCamelCase				   =  XGLMConfig(
		    vocab_size=UpperCamelCase			,       max_position_embeddings=args.max_target_positions			,       num_layers=args.decoder_layers			,       attention_heads=args.decoder_attention_heads			,       ffn_dim=args.decoder_ffn_embed_dim			,       d_model=args.decoder_embed_dim			,       layerdrop=args.decoder_layerdrop			,       dropout=args.dropout			,       attention_dropout=args.attention_dropout			,       activation_dropout=args.activation_dropout			,       activation_function='gelu'			,       scale_embedding=not args.no_scale_embedding			,       tie_word_embeddings=args.share_decoder_input_output_embed			,       )

		_lowerCamelCase				   =  XGLMForCausalLM(UpperCamelCase							)
		_lowerCamelCase				   =  model.load_state_dict(UpperCamelCase			,       strict=UpperCamelCase							)
		print(UpperCamelCase							)
		_lowerCamelCase				   =  make_linear_from_emb(model.model.embed_tokens							)

		return model


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				A						=			parser.parse_args()
				A						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
				model.save_pretrained(args.pytorch_dump_folder_path)
A						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
A						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}